Bridging Equilibrium and Kinetics Prediction with a Data-Weighted Neural Network Model of Methane Steam Reforming

TL;DR

This paper introduces a neural network model that unifies kinetic and equilibrium predictions for methane steam reforming, enabling accurate, continuous process simulations for improved reactor design and control.

Contribution

A novel neural network surrogate model trained on diverse data types that accurately predicts methane reforming outcomes across regimes, enhancing process modeling capabilities.

Findings

High predictive accuracy with mean squared error of 0.000498

Strong correlation coefficients of 0.927 indicating reliable predictions

Model's ability to provide continuous derivatives for optimization

Abstract

Hydrogen's role is growing as an energy carrier, increasing the need for efficient production, with methane steam reforming being the most widely used technique. This process is crucial for applications like fuel cells, where hydrogen is converted into electricity, pushing for reactor miniaturization and optimized process control through numerical simulations. Existing models typically address either kinetic or equilibrium regimes, limiting their applicability. Here we show a surrogate model capable of unifying both regimes. An artificial neural network trained on a comprehensive dataset that includes experimental data from kinetic and equilibrium experiments, interpolated data, and theoretical data derived from theoretical models for each regime. Data augmentation and assigning appropriate weights to each data type enhanced training. After evaluating Bayesian Optimization and Random Sampling, the optimal model demonstrated high predictive accuracy for the composition of the post-reaction mixture under varying operating parameters, indicated by a mean squared error of 0.000498 and strong Pearson correlation coefficients of 0.927. The network's ability to provide continuous derivatives of its predictions makes it particularly useful for process modeling and optimization. The results confirm the surrogate model's robustness for simulating methane steam reforming in both kinetic and equilibrium regimes, making it a valuable tool for design and process optimization.